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•Investigated resolution-SNR trade-off in diffusion MRI of the brain at 7T using EPI, partial Fourier EPI, and spiral.•NMR field probes were used to minimize artifacts due to eddy currents and field non-uniformities.•For matched nominal resolutions, EPI has the highest effective resolution, specificity, and sharpening due to T2* decay.•For matched effective resolutions, spirals offer highest SNR efficiency.•Spiral trajectories are optimal for high-resolution diffusion MRI at 7T. Diffusion MRI (dMRI) is a valuable imaging technique to study the connectivity and microstructure of the brain in vivo. However, the resolution of dMRI is limited by the low signal-to-noise ratio (SNR) of this technique. Various multi-shot acquisition strategies have been developed to achieve sub-millimeter resolution, but they require long scan times which can be restricting for patient scans. Alternatively, the SNR of single-shot acquisitions can be increased by using a spiral readout trajectory to minimize the sequence echo time. Imaging at ultra-high fields (UHF) could further increase the SNR of single-shot dMRI; however, the shorter T2* of brain tissue and the greater field non-uniformities at UHFs will degrade image quality, causing image blurring, distortions, and signal loss. In this study, we investigated the trade-off between the SNR and resolution of different k-space trajectories, including echo planar imaging (EPI), partial Fourier EPI, and spiral trajectories, over a range of dMRI resolutions at 7T. The effective resolution, spatial specificity and sharpening effect were measured from the point spread function (PSF) of the simulated diffusion sequences for a nominal resolution range of 0.6–1.8 mm. In-vivo partial brain scans at a nominal resolution of 1.5 mm isotropic were acquired using the three readout trajectories to validate the simulation results. Field probes were used to measure dynamic magnetic fields offline up to the 3rd order of spherical harmonics. Image reconstruction was performed using static ΔB0 field maps and the measured trajectories to correct image distortions and artifacts, leaving T2* effects as the primary source of blurring. The effective resolution was examined in fractional anisotropy (FA) maps calculated from a multi-shell dataset with b-values of 300, 1000, and 2000 s/mm2 in 5, 16, and 48 directions, respectively. In-vivo scans at nominal resolutions of 1, 1.2, and 1.5 mm were acquired and the SNR of the different trajectories calculated using the multiple replica method to investigate the SNR. Finally, in-vivo whole brain scans with an effective resolution of 1.5 mm isotropic were acquired to explore the SNR and efficiency of different trajectories at a matching effective resolution. FA and intra-cellular volume fraction (ICVF) maps calculated using neurite orientation dispersion and density imaging (NODDI) were used for the comparison. The simulations and in vivo imaging results showed that for matching nominal resolutions, EPI trajectories had the highest specificity and effective resolution with maximum image sharpening effect. However, spirals have a significantly higher SNR, in particular at higher resolutions and even when the effective image resolutions are matched. Overall, this work shows that the higher SNR of single-shot spiral trajectories at 7T allows us to achieve higher effective resolutions compared to EPI and PF-EPI to map the microstructure and connectivity of small brain structures.

In this work we use non-negative matrix factorization to identify patterns of microstructural variance in the human hippocampus. We utilize high-resolution structural and diffusion magnetic resonance imaging data from the Human Connectome Project to query hippocampus microstructure on a multivariate, voxelwise basis. Application of non-negative matrix factorization identifies spatial components (clusters of voxels sharing similar covariance patterns), as well as subject weightings (individual variance across hippocampus microstructure). By assessing the stability of spatial components as well as the accuracy of factorization, we identified 4 distinct microstructural components. Furthermore, we quantified the benefit of using multiple microstructural metrics by demonstrating that using three microstructural metrics (T1-weighted/T2-weighted signal, mean diffusivity and fractional anisotropy) produced more stable spatial components than when assessing metrics individually. Finally, we related individual subject weightings to demographic and behavioural measures using a partial least squares analysis. Through this approach we identified interpretable relationships between hippocampus microstructure and demographic and behavioural measures. Taken together, our work suggests non-negative matrix factorization as a spatially specific analytical approach for neuroimaging studies and advocates for the use of multiple metrics for data-driven component analyses. •We use OPNMF to identify 4 distinct microstructural components in the hippocampus.•Using multiple microstructure metrics improved spatial stability of components.•Variability of component level microstructure related to demographics and cognition.

The neuromodulatory subcortical nuclei within the isodendritic core (IdC) are the earliest sites of tauopathy in Alzheimer’s disease (AD). They project broadly throughout the brain’s white matter. We investigated the relationship between IdC microstructure and whole-brain white matter microstructure to better understand early neuropathological changes in AD. Using multiparametric quantitative magnetic resonance imaging we observed two covariance patterns between IdC and white matter microstructure in 133 cognitively unimpaired older adults (age 67.9 ± 5.3 years) with familial risk for AD. IdC integrity related to 1) whole-brain neurite density, and 2) neurite orientation dispersion in white matter tracts known to be affected early in AD. Pattern 2 was associated with CSF concentration of phosphorylated-tau, indicating AD specificity. Apolipoprotein-E4 carriers expressed both patterns more strongly than non-carriers. IdC microstructure variation is reflected in white matter, particularly in AD-affected tracts, highlighting an early mechanism of pathological development. The isodendritic core is a group of neuromodulatory nuclei with diffuse projections. Here the authors describe associations between the microstructural integrity of the isodendritic core and whole-brain white matter in humans, and its relationship to Apolipoprotein-E4 carrier status.

Functional specialization of brain areas and subregions, as well as their integration into large-scale networks, are key principles in neuroscience. Consolidating both local and global perspectives on cortical organization, however, remains challenging. Here, we present an approach to integrate inter- and intra-areal similarities of microstructure, structural connectivity, and functional interactions. Using high-field in-vivo 7 tesla (7 T) Magnetic Resonance Imaging (MRI) data and a probabilistic post-mortem atlas of cortical cytoarchitecture, we derive multimodal gradients that capture cortex-wide organization. Inter-areal similarities follow a canonical sensory-fugal gradient, linking cortical integration with functional diversity across tasks. However, intra-areal heterogeneity does not follow this pattern, with greater variability in association cortices. Findings are replicated in an independent 7 T dataset and a 100-subject 3 tesla (3 T) cohort. These results highlight a robust coupling between local arealization and global cortical motifs, advancing our understanding of how specialization and integration shape human brain function. How does the brain balance specialization and integration? Using high-resolution MRI and a post-mortem atlas, this study reveals how local and global cortical organization are linked, shaping functional diversity across brain areas.

•Micapipe is a comprehensive pipeline to process multimodal MRI data.•Micapipe generates matrices describing cortico-cortical microstructural similarity, functional connectivity, structural connectivity, and spatial proximity.•The pipeline provides easy-to-verify outputs and visualizations for quality control.•Outputs are hierarchically organized with BIDS-conform naming.•Our evaluations show reproducible processing across several 3T and 7T datasets. Multimodal magnetic resonance imaging (MRI) has accelerated human neuroscience by fostering the analysis of brain microstructure, geometry, function, and connectivity across multiple scales and in living brains. The richness and complexity of multimodal neuroimaging, however, demands processing methods to integrate information across modalities and to consolidate findings across different spatial scales. Here, we present micapipe, an open processing pipeline for multimodal MRI datasets. Based on BIDS-conform input data, micapipe can generate i) structural connectomes derived from diffusion tractography, ii) functional connectomes derived from resting-state signal correlations, iii) geodesic distance matrices that quantify cortico-cortical proximity, and iv) microstructural profile covariance matrices that assess inter-regional similarity in cortical myelin proxies. The above matrices can be automatically generated across established 18 cortical parcellations (100–1000 parcels), in addition to subcortical and cerebellar parcellations, allowing researchers to replicate findings easily across different spatial scales. Results are represented on three different surface spaces (native, conte69, fsaverage5), and outputs are BIDS-conform. Processed outputs can be quality controlled at the individual and group level. micapipe was tested on several datasets and is available at https://github.com/MICA-MNI/micapipe, documented at https://micapipe.readthedocs.io/, and containerized as a BIDS App http://bids-apps.neuroimaging.io/apps/. We hope that micapipe will foster robust and integrative studies of human brain microstructure, morphology, function, cand connectivity.

Misophonia, a disorder characterized by extreme aversion to certain sounds, affects 5%–20% of the general population, yet mechanisms are still largely unknown. Recent neuroimaging studies have reported abnormal functional connectivity of the anterior insula to various limbic, salience, and motor regions in smaller samples of misophonic individuals versus controls, suggesting potential differences in underlying attentional or emotional processes. These findings prompt questions about the insular connectivity profile in larger samples of adults, what patterns emerge when the samples span a wider range of misophonia severity, and how these patterns may or may not overlap with other co‐occurring disorders. To address these questions, we analyzed resting‐state functional magnetic resonance imaging data from the open Welsh Advanced Neuroimaging Database ( N = 162) comprising participants recruited from the general adult population and assessed for sensory sensitivity, anxiety, depression, and autistic traits. A misophonia severity score was derived from the sensory sensitivity data using a model trained on a second adult self‐report sample from Oklahoma ( N = 777). Using anterior insula as a seed for a whole‐brain seed‐to‐voxel connectivity analysis, the derived misophonia severity scores were found to be significantly related to connectivity from the insula to clusters overlapping the planum temporale, operculum, precentral gyrus, and supplementary motor area. Notably, this insular connectivity profile was unique to the anterior insula of the salience network and was not observed when dividing the sample into misophonia (patient) versus control groups, or when grouping participants as a function of anxiety, depression, or autistic traits. These results underline the importance of the salience‐network anterior insula in understanding misophonic aversion and provide tentative evidence of neurological differences between misophonia and anxiety, depression, and autism. This work aids in our understanding of neural mechanisms of misophonia and emphasizes the benefit of treating misophonia as a continuous spectrum disorder to better reflect the variability of symptoms in the real world.

The superficial white matter (SWM), immediately beneath the cortical mantle, is thought to play a major role in cortico-cortical connectivity as well as large-scale brain function. Yet, this compartment remains rarely studied due to its complex organization. Our objectives were to develop and disseminate a robust computational framework to study SWM organization based on 3D histology and high-field 7T MRI. Using data from the BigBrain and Ahead 3D histology initiatives, we first interrogated variations in cell staining intensities across different cortical regions and different SWM depths. These findings were then translated to in vivo 7T quantitative myelin-sensitive MRI, including T1 relaxometry (T1 map) and magnetization transfer saturation (MTsat). As indicated by the statistical moments of the SWM intensity profiles, the first 2 mm below the cortico-subcortical boundary were characterized by high structural complexity. We quantified SWM microstructural variation using a nonlinear dimensionality reduction method and examined the relationship of the resulting microstructural gradients with indices of cortical geometry, as well as structural and functional connectivity. Our results showed correlations between SWM microstructural gradients, as well as curvature and cortico-cortical functional connectivity. Our study provides novel insights into the organization of SWM in the human brain and underscores the potential of SWM mapping to advance fundamental and applied neuroscience research.

Statistical effects of cortical metrics derived from standard T1‐ and T2‐weighted magnetic resonance imaging (MRI) images, such as gray–white matter contrast (GWC), boundary sharpness coefficient (BSC), T1‐weighted/T2‐weighted ratio (T1w/T2w), and cortical thickness (CT), are often interpreted as representing or being influenced by intracortical myelin content with little empirical evidence to justify these interpretations. We first examined spatial correspondence with more biologically specific microstructural measures, and second compared between‐marker age‐related trends with the underlying hypothesis that different measures primarily driven by similar changes in myelo‐ and microstructural underpinnings should be highly related. Cortical MRI markers were derived from MRI images of 127 healthy subjects, aged 18–81, using cortical surfaces that were generated with the CIVET 2.1.0 pipeline. Their gross spatial distributions were compared with gene expression‐derived cell‐type densities, histology‐derived cytoarchitecture, and quantitative R1 maps acquired on a subset of participants. We then compared between‐marker age‐related trends in their shape, direction, and spatial distribution of the linear age effect. The gross anatomical distributions of cortical MRI markers were, in general, more related to myelin and glial cells than neuronal indicators. Comparing MRI markers, our results revealed generally high overlap in spatial distribution (i.e., group means), but mostly divergent age trajectories in the shape, direction, and spatial distribution of the linear age effect. We conclude that the microstructural properties at the source of spatial distributions of MRI cortical markers can be different from microstructural changes that affect these markers in aging. Cortical MRI markers and their statistical effects are often thought to be driven by intracortical myelin. We report higher spatial relations of these markers with indicators of myelin and glial cells compared to neuronal cells. Comparing markers between themselves, we show generally high spatial overlap but divergent age trajectories, highlighting that the microstructural properties at the source of spatial distributions of MRI cortical markers can be different from microstructural changes that affect these markers in aging.

Alterations in the myelination of the cerebral cortex may underlie abnormal cortical function in a variety of brain diseases. Here, we describe a technique for investigating changes in intracortical myelin in clinical populations on the basis of cortical thickness measurements with magnetic resonance imaging (MRI) at 3 Tesla. For this, we separately compute the thickness of the shallower, lightly myelinated portion of the cortex and its deeper, heavily myelinated portion (referred to herein as unmyelinated and myelinated cortex, respectively). Our expectation is that the thickness of the myelinated cortex will be a specific biomarker for disruptions in myeloarchitecture. We show representative atlases of total cortical thickness, T, unmyelinated cortical thickness, G, and myelinated cortical thickness, M, for a healthy group of 20 female subjects. We further demonstrate myelinated cortical thickness measurements in a preliminary clinical study of 10 bipolar disorder type-I subjects and 10 healthy controls, and report significant decreases in the middle frontal gyrus in T, G, and M in the disorder, with the largest percentage change occurring in M. This study highlights the potential of myelinated cortical thickness measurements for investigating intracortical myelin involvement in brain disease at clinically relevant field strengths and resolutions.

Decreased long‐range temporal correlations (LRTC) in brain signals can be used to measure cognitive effort during task execution. Here, we examined how learning a motor sequence affects long‐range temporal memory within resting‐state functional magnetic resonance imaging signal. Using the Hurst exponent (HE), we estimated voxel‐wise LRTC and assessed changes over 5 consecutive days of training, followed by a retention scan 12 days later. The experimental group learned a complex visuomotor sequence while a complementary control group performed tightly matched movements. An interaction analysis revealed that HE decreases were specific to the complex sequence and occurred in well‐known motor sequence learning associated regions including left supplementary motor area, left premotor cortex, left M1, left pars opercularis, bilateral thalamus, and right striatum. Five regions exhibited moderate to strong negative correlations with overall behavioral performance improvements. Following learning, HE values returned to pretraining levels in some regions, whereas in others, they remained decreased even 2 weeks after training. Our study presents new evidence of HE's possible relevance for functional plasticity during the resting‐state and suggests that a cortical subset of sequence‐specific regions may continue to represent a functional signature of learning reflected in decreased long‐range temporal dependence after a period of inactivity. The present study highlights the significance of using long‐range temporal correlations (LRTC) within the rsfMRI BOLD signal as a potential sensitive biomarker for functional neuroplasticity. Our findings demonstrate that decreases in LRTC reflect sequence‐specific motor learning and performance improvements, and that these changes persist even after a two‐week break from training. These results suggest that alterations in functional dynamics represent the newly learned skill and support the use of LRTC as a sensitive measure of functional neuroplasticity resulting from complex motor learning.